• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.283-295
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• 2024

Many studies have been conducted to properly handle nonresponse. Recently, many nonresponse imputation methods have been developed and practically used. Most imputation methods assume MCAR (missing completely at random) or MAR (missing at random). On the contrary, there are relatively few studies on imputation under the assumption of MNAR (missing not at random) or NN (nonignorable nonresponse) that are affected by the study variable. The MNAR causes Bias and reduces the accuracy of imputation whenever response probability is not properly estimated. Lee and Shin (2022) proposed a nonresponse imputation method that can be applied to nonignorable nonresponse assuming homoscedasticity in super-population model. In this paper we propose an generalized version of the imputation method proposed by Lee and Shin (2022) to improve the accuracy of estimation by removing the Bias caused by MNAR under heteroscedasticity. In addition, the superiority of the proposed method is confirmed through simulation studies.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.